Design Model of Concrete Mix Proportion Based on the Cost Performance Optimization

被引：0
|
作者：
Jiao C. [1 ]
Cui L. [1 ]
Gao R. [2 ]
Guo W. [3 ]
Song D. [2 ]
机构：
[1] School of Civil Engineering, Guangzhou University, Guangzhou
[2] Zhongshan DongJun Concrete Co., Ltd., Zhongshan
[3] Zhuhai Chunhe New Material Research Institute Co., Ltd., Zhuhai
来源：
Jianzhu Cailiao Xuebao/Journal of Building Materials | 2020年 / 23卷 / 06期
关键词：
Concrete; Cost control; Model; Optimization of mix proportion; Performance-price ratio;
D O I：
10.3969/j.issn.1007-9629.2020.06.009
中图分类号：
学科分类号：
摘要：
The concrete mix proportion of 502groups was collected as training data. A concrete formula design model which can be used to control concrete cost and formula optimization was constructed based on BP neural network, genetic algorithm and particle swarm optimization algorithm. In this model the cost of raw materials of concrete and the several key factors influencing the compressive strength of concrete were taken into consideration, the penalty function was introduced to the particle swarm algorithm for the fitness value of the objective function of punishment. They were used to solve the concrete formula in the design of nonlinear constraints discrete variables and continuous variables, and may be used to control concrete cost and optimize the mix proportion. The 27 sets of concrete mix proportions after cost reduction were obtained, and the compressive strength test was conducted by the established model. The test results show that the combined ratio cost to the target cost is close to 97%, when the concrete cost per cubic meter is reduced by 5 yuan, 10 yuan and 15 yuan, the performance of mix proportion exported by the model meets the strength requirements. © 2020, Editorial Department of Journal of Building Materials. All right reserved.
引用
收藏
页码：1321 / 1327
页数：6
相关论文
共 15 条
  • [1] XU Guoqiang, SU Youpo, HAN Dianli, Et al., Prediction model of green concrete compressive strength based on BP neural network, Concrete, 2, pp. 33-35, (2013)
  • [2] SALAZAR F, TOLEDO M A, ONATE E, Et al., An empirical comparison of machine learning techniques for dam behaviour modelling, Structural Safety, 56, 5, pp. 9-17, (2015)
  • [3] WANG Fenglai, ZHU Fei, ZHOU Qiang, Prediction of compressive strength of pothole block masonry based on BP neural network, Journal of Building Materials, 18, 6, pp. 1010-1016, (2015)
  • [4] SU H, LI X, YANG B, Et al., Wavelet support vector machine-based prediction model of dam deformation, Mechanical Systems and Signal Processing, 110, 3, pp. 412-427, (2018)
  • [5] LI Zhongxin, WEI Zhuobin, SHEN Jinlin, Prediction model of compressive strength of coral concrete based on BP neural network, Concrete, 1, pp. 64-69, (2016)
  • [6] NAWROWSKI R, LORENC J, NOWAK L, Et al., Constrained optimization using penalty function method combined with genetic algorithm, Computer Applications in Electrical Engineering, 19, 1, pp. 2097-2271, (2018)
  • [7] KNYPINSKI L, KOWALSKI K, FENN R, Et al., Constrained optimization of the magnetostrictive actuator with the use of penalty function method, Compel-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 37, 5, pp. 1575-1584, (2018)
  • [8] ZHANG H, YU X., Research on oil and gas pipeline defect recognition based on IPSO for RBF neural network, Sustainable Computing: Informatics and Systems, 20, 1, pp. 203-209, (2018)
  • [9] TAYFURG E T K, KIRCA O., Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks, Journal of Materials in Civil Engineering, 26, 11, pp. 401-407, (2014)
  • [10] HAN K, QASIM S, SHEIKH M, Et al., Predicting strength and strain enhancement ratios of circular fiber-reinforced polymer tube confined concrete under axial compression using artificial neural networks, Advances in Structural Engineering, 22, 5, pp. 1426-1443, (2019)
12